The Evaluation of Systemic Racism and its Present-Day Integration in Society
Bibliographic record
Abstract
Rinku Sen, executive director of Race Forward, defines "systemic racism," which is also known as "institutional racism," as "a form of racism that is embedded as the normal practice within society or standard operating procedures within an organization." This paper combines theory and practice by showing the relationship between systemic racism and the effects it has on the lives of real people around the world. Further, by examining racism through a historical lens and its impact on Europe, the United States, Canada, the Caribbean, and Latin America, it will become clear that systemic racism is a global phenomenon best understood through the transdisciplinary approach of global studies. Two cases will be used to illustrate specific effects of systemic racism in the world: health care and policing. Relatedly, these cases showcase how systemic racism impacts its victims, primarily ethnic minorities and the new poor. Because systemic racism is often overlooked or denied by those who do not themselves feel victimized by it, this paper uses a transdisciplinary approach to make it visible. Especially relevant to this project will be sociology, global history, political science, and criminal justice literatures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".